Geometrical compensation models

ABSTRACT

In an example, a method includes acquiring, by a processor and for a set of locations in a fabrication chamber for additive manufacturing, data identifying a first subset of locations associated with a first level of variability in deformations in object generation and a second subset of locations associated with a second, greater, level of variability in deformations object generation. A geometrical compensation model may be derived to compensate for anticipated deformations in object generation by a first additive manufacturing apparatus. The geometrical compensation model may comprise geometrical transformations to apply to object model data representing at least a portion of an object, wherein each geometrical transformation is associated with a location of the set of locations. The first subset of locations may be associated with geometrical transformations determined based objects generated by a plurality of additive manufacturing apparatus and the second subset of locations may be associated with geometrical transformations determined based on objects generated by the first additive manufacturing apparatus.

BACKGROUND

Additive manufacturing techniques may generate a three-dimensionalobject through the solidification of a build material, for example on alayer-by-layer basis. In examples of such techniques, build material maybe supplied in a layer-wise manner and the solidification method mayinclude heating the layers of build material to cause melting inselected regions. In other techniques, chemical solidification methodsmay be used.

BRIEF DESCRIPTION OF DRAWINGS

Non-limiting examples will now be described with reference to theaccompanying drawings, in which:

FIG. 1 shows an example method of deriving a geometrical compensationmodel;

FIG. 2A shows locations within a fabrication chamber and FIGS. 2B and 2Cshow scaling and offset transformations derived for two of thelocations;

FIG. 3 shows another example method of deriving a geometricalcompensation models and generating objects;

FIG. 4 shows an example method of deriving geometrical transformations;in

FIGS. 5 and 6 are simplified schematic drawings of example apparatus;

and

FIG. 7 is a simplified schematic drawing of an example machine-readablemedium associated with a processor.

DETAILED DESCRIPTION

Additive manufacturing techniques may generate a three-dimensionalobject through the solidification of a build material. In some examples,the build material is a powder-like granular material, which may forexample be a plastic, ceramic or metal powder and the properties ofgenerated objects may depend on the type of build material and the typeof solidification mechanism used. In some examples the powder may beformed from, or may include, short fibres that may, for example, havebeen cut into short lengths from long strands or threads of material.Build material may be deposited, for example on a print bed andprocessed layer by layer, for example within a fabrication chamber.According to one example, a suitable build material may be PA12 buildmaterial commercially referred to as V1R10A “HP PA12” available from HPInc.

In some examples, selective solidification is achieved using heat, forexample through directional application of energy, for example using alaser or electron beam which results in solidification of build materialwhere the directional energy is applied. In other examples, at least oneprint agent may be selectively applied to the build material, and may beliquid when applied. For example, a fusing agent (also termed a‘coalescence agent’ or ‘coalescing agent’) may be selectivelydistributed onto portions of a layer of build material in a patternderived from data representing a slice of a three-dimensional object tobe generated (which may for example be generated from structural designdata). The fusing agent may have a composition which absorbs energy suchthat, when energy (for example, heat) is applied to the layer, the buildmaterial heats up, coalesces and solidifies upon cooling, to form aslice of the three-dimensional object in accordance with the pattern. Inother examples, coalescence may be achieved in some other manner.

According to one example, a suitable fusing agent may be an ink-typeformulation comprising carbon black, such as, for example, the fusingagent formulation commercially referred to as V1Q60A “HP fusing agent”available from HP Inc. In examples, such a fusing agent may comprise anyor any combination of an infra-red light absorber, a near infra-redlight absorber, a visible light absorber and a UV light absorber.Examples of print agents comprising visible light absorption enhancersare dye based colored ink and pigment based colored ink, such as inkscommercially referred to as CE039A and CE042A available from HP Inc.

In addition to a fusing agent, in some examples, a print agent maycomprise a coalescence modifier agent, which acts to modify the effectsof a fusing agent for example by reducing or increasing coalescence orto assist in producing a particular finish or appearance to an object,and such agents may therefore be termed detailing agents. In someexamples, detailing agent may be used near edge surfaces of an objectbeing printed, and may for example act to cool the build material towhich it is applied, or otherwise to reduce or prevent coalescencethereof. According to one example, a suitable detailing agent may be aformulation commercially referred to as V1Q61A “HP detailing agent”available from HP Inc. A coloring agent, for example comprising a dye orcolorant, may in some examples be used as a fusing agent or acoalescence modifier agent, and/or as a print agent to provide aparticular color for the object.

As noted above, additive manufacturing systems may generate objectsbased on structural design data. This may involve a designer generatinga three-dimensional model of an object to be generated, for exampleusing a computer aided design (CAD) application. The model may definethe solid portions of the object. To generate a three-dimensional objectfrom the model using an additive manufacturing system, the model datacan be processed to derive slices of parallel planes of the model. Eachslice may define a portion of a respective layer of build material thatis to be solidified or caused to coalesce by the additive manufacturingsystem.

FIG. 1 is an example of a method, which may comprise a computerimplemented method of deriving a geometrical compensation model for usein additive manufacturing, wherein the model is derived or adapted for aparticular object generation apparatus.

Geometrical transformations may be used to modify object model data, forexample to apply a geometrical compensation in order to compensate foranticipated departures from intended dimensions when generating anobject. The transformation to apply may be provided by use of ageometrical compensation model, which may specify a value of at leastone geometrical transformation parameter.

For example, it may be the case that, in some print apparatus, when anobject is generated in a process which includes heat, additional buildmaterial may adhere to the object on generation. In one example, fusingagent may be associated with a region of the layer which is intended tofuse. However, when energy is supplied, build material of neighbouringregions may become heated and fuse to the outside of the object (in someexamples, being fully or partially melted, or adhering to melted buildmaterial as powder). Therefore, a dimension of an object may be largerthan the region(s) to which fusing agent is applied. In order tocompensate for this effect, i.e. where it is anticipated that an objectmay tend to ‘grow’ during manufacture in this (or some other) manner,the object volume as described in object model data may be reduced tocompensate for such growth. The reduction of the volume may be definedin a geometrical compensation (or geometrical transformation) model asat least one geometrical compensation parameter value.

In other examples, objects may be smaller following object generationthan is specified in object model data. For example, some buildmaterials used to generate objects may shrink on cooling. Therefore, ageometrical transformation model may specify at least one geometricaltransformation parameter value to in turn specify how an object volumein object model data should be increased to compensate for theanticipated reduction in volume.

A particular object may be subject to mechanisms which result in growthand/or shrinkage, and the actual compensation to be applied may bedetermined by consideration of, or may be influenced by, the differentdegrees to which an object may be affected by such processes.

In some examples, a modification may be specified using a geometricaltransformation parameter(s) comprising a scaling and/or an offset (forexample, specified as a scaling factor and/or an offset value). Ascaling factor may be used to multiply all specified dimensions in thedirection of at least one axis by a value, which may be greater than 1in order to increase the dimension(s) and less than 1 to reduce thedimension(s). An offset value may specify, for example by a specifieddistance or a number of defined sub volumes or ‘voxels’ (i.e.three-dimensional pixels), an amount to add or remove from a surface ofthe object (or a perimeter within a layer). For example, a distance fromthe object surface may be specified and the object may be eroded ordilated (i.e., inflated or enlarged) by this distance.

Geometrical compensations may in general be determined based on atheoretical analysis of object generation, for example considering thepredicted temperatures and/or material behaviour characteristics. Insome examples, geometrical compensation models may for example bederived based on test objects, which may be generated (i.e. manufacturedor ‘printed’) and measured to characterise the deformations. Techniquessuch as data fitting or regression may be used to determine the scalingfactor and/or offset to apply. For example, where previously generatedobjects have tended to be smaller than expected in a particulardimension, there may be a scaling factor of greater than 1 and/or apositive offset derived and where previously generated objects havetended to be larger than expected in a particular dimension, there maybe a scaling factor of less than 1 and/or a negative offset derived. Insome examples, geometrical compensation models/geometricaltransformations may be derived using machine learning techniques basedon deviations from expected dimensions in previously generated objects.

One example of a geometrical compensation model may comprise one or aset of scaling and/or offset parameters associated with a particularobject generation apparatus, or type of object generation apparatus. Theparameters may be applied to all objects in the same way (for example,regardless of the object size and/or placement).

In other examples, a geometrical compensation model may allow ageometrical compensation derived or selected therefrom to be tailored toan intended object generation operation and/or object.

In particular, in examples herein, a geometrical compensation modeltakes account of an intended location of an object in a fabricationchamber of an additive manufacturing apparatus. It has been noted thatdimensional deformation may be related to the location of objectgeneration within a chamber, and therefore different compensations maybe applied for different object locations to improve accuracy. Suchgeometrical compensation models may therefore comprise or providetransformations which may be mapped to the intended location of anobject (which may for example be a single identifiable point such as thelocation of the centre of mass of the object, or may include aconsideration of the volumetric extent of the object).

For example, if an object is to be generated at a first location withinthe fabrication chamber, the location may be mapped to a geometricaltransformation comprising one or more offset and/or scaling values whichare intended to compensate for anticipated deviations from intendeddimensions. However, if the same object were to be generated at a secondlocation within the fabrication chamber, this second location may bemapped to a different geometrical transformation comprising one or moredifferent offset and/or scaling values. Thus, the particular geometricaltransformation applied may vary between different locations, and may bebased on predetermined mappings or the like.

In examples therefore, a geometrical compensation model may comprise aplurality of defined geometrical transformation parameters (or parametersets), each associated with different locations within the fabricationchamber. In such examples, a particular geometrical transformationparameter(s) and/or value(s) may be selected based on the intendedobject generation location. In some examples, defined locations or‘nodes’ may be associated with geometrical transformationparameter(s)/value(s), and the geometrical transformationparameter(s)/value(s) to apply at locations intermediate to such definedlocations may be derived for example by interpolation, or by selectionof the closest defined location, or the like. The ‘nodes’ may forexample be associated with locations distributed to form intersectionsof a grid within the fabrication chamber such that they are dispersed(for example, regularly) throughout the chamber. The model may beembodied as a look-up table or other mapping resource, mapping thelocations to parameter values to be applied to the object models ofobjects to be generated at the location.

In some examples, other characteristics of the object, such asconsideration of the object volume and/or surface area, may be used asinput parameters in a geometrical compensation model, and may be mappedto different geometrical transformation values.

For example, bulkier objects (i.e. objects having a larger volume) mayaccrue greater thermal energy than smaller objects, and may thereforetend to accumulate more heat than smaller objects. Cooling such objectsmay therefore take more time than cooling less bulky objects. This maylead to different deformations. The surface area (and combinations ofthe volume and surface area) may be used to determine how ‘solid’ anobject is. The amount of solid material in an object may be used topredict how the object may deform. For example, a more solid object maytend to accumulate more heat than a less solid object in a thermalfusing additive manufacturing operation. Such object generationparameters may therefore be mapped to different geometricaltransformations within a geometrical compensation model.

Some geometrical compensation models may for example include aconsideration of how many objects are to be generated in a fabricationchamber and/or the proximity of the objects (for example in terms of‘packing density’).

In some examples, other object generation parameter values (which may beobject generation parameter values which are configurable or selectableby a user or operator) may also be considered. The object generationparameter(s) may be any parameter which may have an impact ondimensional inaccuracy. For example, the object generation parameter(s)may comprise any, or any combination of, environmental conditions,object generation apparatus, object generation material composition(which may comprise selection of the type or composition of buildmaterial and/or print agents), object cooling profile, print mode, orthe like. These may be specified, for example, by input to at least oneprocessor. Thus, different geometrical compensation models and/ordifferent parameters may be provided for different apparatus, differentprint modes, different cooling profiles or the like.

The geometrical transformation parameter value(s) and/or geometricalcompensation model(s) specifying such parameters may for example bestored in a memory, for example embodied as a mapping resource(s) suchas lookup tables and the like, or may be embodied as one or morealgorithm, for example relating object generation parameter(s) (e.g.object generation location and in some examples any or any combinationof volume, surface area, packing density, environmental conditions,object generation apparatus, object generation material composition,object cooling profile or print mode or the like) to a compensation tobe applied to object model data.

The method of FIG. 1 comprises, in block 102, acquiring, by a processorand for a set of locations in a fabrication chamber for additivemanufacturing, data identifying a first subset of locations associatedwith a first level of variability in deformations in object generationand a second subset of locations associated with a second level ofvariability in deformations object generation, wherein the second levelis greater than the first level. The data may for example be acquiredfrom a memory, or over a network, determined by analysis of other data(for example, data generated by measuring objects generated by a numberof different additive manufacturing apparatus) or the like. Each subsetmay comprise one or more location. In some examples, the acquiring thedata may comprise receiving the data over a network or retrieving thedata from a memory. In some examples, acquiring the data may comprisesperforming measurements.

As mentioned above, the location of object generation within afabrication chamber may have an impact on object deformation. Therefore,compensation models which associate object generation location with oneor more compensation parameters may be developed. Generating such modelsmay comprise determining an ‘average’ compensation to apply over aplurality of build operations, which may be performed using a pluralityof additive manufacturing operations. The ‘average’ may in practice bederived as mentioned above using regression/data fitting, and mayrepresent a ‘best fit’ for previously manufactured and measured objects.

A geometrical compensation model may comprise one or a plurality ofpredefined geometrical transformations, and/or may comprise informationused to derive geometrical transformation(s) to apply to object modeldata describing an object. In some examples, in addition to location,the model may be associated with expected input parameter(s) (such asobject volume, object surface area, build material, object generationparameters and the like) which are to be provided to determinegeometrical transformation(s) to apply to an object. For example, theremay be a mapping between such input parameter(s) and geometricaltransformation(s).

In order to characterise the deformations of a particular additivemanufacturing apparatus, one option may be to generate at least one setof calibration objects distributed through the volume of a fabricationchamber. The calibration objects of such a ‘calibration batch’ may bemeasured and the measurements used in determining or tailoring acompensation model to that apparatus. This can result in a well-designedmodel, which is likely to result in the generation of objects withaccurate dimensions. However, in practice, to provide a good model,several batches of calibration objects may be generated, and each batchtakes time and utilises resources such as build material and printagents, increasing costs. Measurements of the objects themselves may betime consuming, for example being a manual process or performed byspecialist equipment.

However, the inventors have realised that locations within fabricationchambers may be associated with a level of variability in deformations.For example, for a first location within the fabrication, thedeformations measured over a number of different fabrication operations(which may be in the same apparatus or multiple different apparatus) maybe relatively consistent-there may be little spread in the datacollected for objects generated in such locations. This consistencywould therefore also be seen for the scaling and offset factorsgenerated based on each build operation. A measure of the spread, forexample the standard deviation of the data characterising deformationsand/or the transformation values, may be relatively low. However, in asecond location, the data may be more variable—expressed another way,the level of variation, for example as characterised by the standarddeviation, may be higher.

Block 104 comprises deriving, by a processor (which may comprise thesame processor as used in block 102), a geometrical compensation modelto compensate for anticipated deformations in object generation by afirst additive manufacturing apparatus. The geometrical compensationmodel comprises geometrical transformations to apply to object modeldata representing at least a portion of an object to be generated by theadditive manufacturing apparatus, wherein each geometricaltransformation is associated with a location of the set of locations. Inthis example, the first subset of locations (i.e. those with a lowerlevel of variability) are associated with geometrical transformationsdetermined based on indications of deviations between intended objectdimensions and generated object dimensions of objects generated by aplurality of additive manufacturing apparatus, and the second subset oflocations (i.e. those with a higher level of variability) are associatedwith geometrical transformations determined based on indications ofdeviations between intended object dimensions and generated objectdimensions of objects generated by the first additive manufacturingapparatus.

In other words, the geometrical compensation model for the firstadditive manufacturing apparatus may comprise two data sources. For thefirst subset of locations (those locations which are associated withmore consistent deformations), ‘generic’ geometrical transformationparameter values may be used. The data for characterising geometricalmodifications to apply to object model data may comprise a data setbased on indications of deviations between intended object dimensionsand generated object dimensions for objects generated by a plurality ofadditive manufacturing apparatus (i.e. data from which a ‘generic’geometrical compensation model may be generated).

However, for the second subset of locations—those associated with highervariability in deformation-‘specific’ geometrical transformationparameter values may be used, i.e. those derived from generating objectsusing the first additive manufacturing apparatus itself.

By identifying the data with a higher level of variability indeformation, the parameters which are most likely to differ betweendifferent instances of additive manufacturing apparatus may also beidentified. In turn, this means that the tasks of generating calibrationobjects using the first additive manufacturing apparatus and measuringsuch objects may be reduced in scope as calibration objects may begenerated by the first additive manufacturing apparatus in at least oneof those location(s) with relatively high variability and not inlocation(s) with relatively low variability.

The first and second levels of variability may for example be determinedbased on a threshold, which may for example be set considering a balancebetween the level of tailoring of a ‘generic’ model, and the generationof specific data for the first additive manufacturing operation. Whiletailoring the data for a higher number of locations will increase theaccuracy of objects generated using the model by the first additivemanufacturing apparatus, generating the data consumes both time andresources. The balance may be predetermined, for example selected basedon an intended manufacturing tolerance or may be user selected or thelike.

Thus, the method of FIG. 1 allows a generic compensation model to betailored intelligently by targeting the location(s) associated with ahigh level of variability, or a lower level of certainty of suitabilityto any given additive manufacturing apparatus, for (resource and timeconsuming) generation of data specific to the first additivemanufacturing apparatus.

Block 104 may for example comprise replacing transformation parametersof a generic compensation model with transformation parameters generatedfrom data acquired from objects generated by the first additivemanufacturing apparatus

In some examples, the geometrical compensation model may have any of thecharacteristics of the geometrical compensation models described aboveand may characterise one or more geometrical transformation to beapplied to object model data. Such geometrical transformations maycomprise at least one of an offset and a scaling factor. For example, ageometrical transformation may be specified as scaling and/or offsetcomponents in an X and Y axis (for example to be applied in a singleslice of an object), or as scaling and/or offset components in an X, Yand Z axis.

In some examples, a geometrical transformation may be defined using twoor three scaling factors (one for each of two/three axes, which may beorthogonal) and/or two or three offset values (one for each of two/threeaxes, which may be orthogonal). If scaling is not indicated in a givenaxis, the scaling factor in relation to that axis may be set to 1, andif no offset is indicated in a given axis, the offset value in relationto that axis may be set to 0.

Taking an example in which a scaling factor is specified in each ofthree orthogonal axes, this may in some examples be specified as avector having components in the X, Y and Z directions, and may forexample be specified as [SFx, SFy, SFz]. This may, for example, takingthe object in its intended generation orientation, mean that the ‘width’of the object is to be scaled by SFx, the ‘depth’ of the object is to bescaled by SFy, and the ‘height’ of the object is to be scaled by SFz(noting that, in practice, the object may be generated in anyorientation, and therefore the height of the object during generationmay not correspond to the height of the object as orientated for usethereof following generation). Similarly, the offset for a givenlocation may be specified as [Ox, Oy, Oz]

FIG. 2A shows an example of a fabrication chamber 200 which has aplurality of defined locations, or nodes 202, 204 (a subset of which islabelled to avoid complicating the Figure). In this example, the nodesare distributed in a regular pattern although this need not be the casein all examples. There are two types of nodes, indicated by theirrespective different shapes. A first type of node 202 is indicated as acircle, and indicates locations associated with a first level ofvariability in deformations in object generation. The second type ofnode 204 is indicated as a square, and is associated with a secondsubset of locations associated with a second, greater, level ofvariability in deformations object generation.

Each of the nodes 202, 204 may be associated with a scaling and/oroffset parameter for objects generated at that location. Generation atthat location may mean that the object has a centre of mass at thatlocation, and compensation parameters for objects to be generated with acentre of mass between the nodes 202, 204 may for example be those ofthe closest node, or generated from one or more of the nodes near to thelocation. However, in principle, any identifiable point on the objectmay be used to define its location.

In this example, the number of nodes shown is relatively small to avoidovercomplicating the Figures. However, in practice, there may beprecomputed geometrical compensation values for many more, or examplearound 2000-5000 positions in an example fabrication chamber having asize of around 380 mm×284 mm×380 mm, and the values for intermediatelocations may be interpolated from these values. While calibrationobjects may be generated or ‘printed’ at each node, and therefore beused to derive data for that node, this may not be the case in practice.In some example, the number of calibration objects, even in a fullcalibration batch (i.e. a build operation which includes calibrationobjects and no production objects), the number of objects may be in thelow hundreds (for example 100-150), and the precomputed node values maybe determined using, for example, linear and/or non-linearinterpolations (which may be based on previously derived patterns, forexample using machine learning and the like).

FIGS. 2B and 2C respectively show an example of scale and offsetparameters which has been determined for each of a plurality of objectsgenerated in a location of a node of the first type 202 (FIG. 2B), andfor objects generated in a location of a node of the second type (FIG.2C), using the same arbitrary scale. Each dot indicates a scaling factorand offset value derived as a result of generating an instance of anobject on a particular apparatus, such that each dot relates to adifferent additive manufacturing apparatus. As can be seen, the dots inFIG. 2B are more concentrated than those shown in FIG. 2C.

FIG. 2C also shows dotted lines leading to a ‘centre of gravity’ of theplot. In some examples, the length of these lines may be used tocalculate a variability parameter. For example, a variability parametermay be calculated as:

$\sqrt{\frac{{\sum_{i = 1}^{n}\left( {\overset{\_}{o} - o_{i}} \right)^{2}} + \left( {\overset{\_}{s} - s_{i}} \right)^{2}}{n - 1}}$

where s_(i) and o_(i) are each value of scale and offset of the part ineach additive manufacturing apparatus i of a set of n such apparatus,and s and ō are the respective average values of scale and offset.

FIG. 3 is an example of a method of determining a geometricalcompensation model, which may be a geometrical compensation model asdescribed in relation to FIG. 1 .

Block 302 comprises receiving a first geometrical compensation modelincluding geometrical transformations for the set of locations. In otherwords, the first geometrical compensation model is a ‘generic’geometrical compensation model. In this example, each location isassociated with a variability parameter derived from measurements ofdeformations of objects generated by the plurality of additivemanufacturing apparatus. In some examples, determining the variabilityparameter for each location may comprise determining a measure ofstandard deviation. The variability parameter may for example be adeviation in the parameters derived for each of a plurality apparatus(as set out in the equation above), or may comprise variability indeformation data directly (i.e. the deviations from expected dimensionsof the object).

Block 304 comprises setting a threshold defining the first and secondlevels of variability. For example, for location(s) where the standarddeviation is above a threshold, these may be designated as having thesecond level of variability and therefore belonging to the second subsetwhereas if the standard deviation is below a threshold, these locationsmay be designated as having the first level of variability and thereforebelonging to the first subset of locations. The threshold may forexample be set based on an intended accuracy for object generation. If ahigh level of accuracy is intended, then the threshold may be set lowerthan if a lower level of accuracy is acceptable.

Block 306 comprises determining object generation instructions forgenerating objects in at least some of the second subset of locations.For example, this may comprise generating additive manufacturinginstructions for a batch of objects, some of which may be objects whichit is intended to be used or sold (referred to as ‘production’ objectsherein), whereas other objects may be intended to be used as calibrationobjects, and such calibration objects may be generated in the secondsubset of locations. While in principle any object could provide anindication of deviations from anticipated dimensions, in some examples,calibration objects may have features which renders them suitable forsuch use, for example having well defined measurement points and/orrelative dimensions. The inclusion of calibration objects may reduce thespace available for production objects, but these may nevertheless begenerated alongside the calibration objects.

In some examples, the data models of the production object(s) may havetransformations applied thereto based on their location and compensationparameters derived from the first geometrical compensation model. Insome examples, the calibration object(s) may also have transformationsapplied thereto based on their location and compensation parametersderived from the first geometrical compensation model, although in otherexample no such compensations may be applied.

Block 308 comprises generating the objects by the first additivemanufacturing apparatus (which may comprise calibration objects at atleast some of the second subset of locations and production objectselsewhere).

Methods of object generation may comprise determining object generationinstructions (or ‘print instructions’) for generating the object. Theobject generation instructions in some examples may specify an amount ofprint agent to be applied to each of a plurality of locations on a layerof build material. For example, deriving object generation instructionsmay comprise determining ‘slices’ of a virtual build volume comprisingvirtual object(s) (to which a modification may have been applied) andrasterising these slices into pixels (or voxels, i.e. three-dimensionalpixels). An amount of print agent (or no print agent) may be associatedwith each of the pixels/voxels. For example, if a pixel relates to aregion of a build volume which is intended to solidify, the objectgeneration instructions may be derived to specify that fusing agentshould be applied to a corresponding region of build material in objectgeneration. If however a pixel relates to a region of the build volumewhich is intended to remain unsolidified, then object generationinstructions may be derived to specify that no agent, or a coalescencemodifying agent such as a detailing agent, may be applied thereto, forexample to cool the build material. In addition, the amounts of suchagents may be specified in the derived instructions and these amountsmay be determined based on, for example, thermal considerations and thelike. In other examples, object generation instructions may specify howto direct directed energy, or how to place a curing or binding agent orthe like.

Generating an object may comprise generating the object based on objectgeneration instructions (or ‘print instructions’). For example, such anobject may be generated layer by layer. For example, this may compriseforming a layer of build material, applying print agents, for examplethrough use of ‘inkjet’ liquid distribution technologies in locationsspecified in the object generation instructions for an object modelslice corresponding to that layer using at least one print agentapplicator, and applying energy, for example heat, to the layer. Sometechniques allow for accurate placement of print agent on a buildmaterial, for example by using print heads operated according to inkjetprinciples of two-dimensional printing to apply print agents, which insome examples may be controlled to apply print agents with a resolutionof around 600 dpi, or 1200 dpi. A further layer of build material maythen be formed and the process repeated, for example with the objectgeneration instructions for the next slice. In other examples, objectsmay be generated using directed energy, or through use of chemicalbinding or curing, or in some other way.

Block 310 comprises acquiring an indication of deviations betweenintended object dimensions and generated object dimensions for objectsgenerated by the first additive manufacturing apparatus. For example,this may comprise measuring a plurality of dimensions of at least onegenerated object, and/or receiving such measurements at a processor. Insome examples, there may be tens or hundreds such data points acquired.In some examples, the measurements and/or the deviations may bedetermined remotely and transmitted to the at least one processor, forexample over a network or the like. Measurements of dimensions may forexample be acquired using at least one of 3D scanning, other opticalmeasurement techniques and/or using manual measurements.

As noted above, in some examples, the indication of deviations betweenintended object dimensions and generated object dimensions for objectswhich are generated by the first additive manufacturing apparatusapplying a geometrical compensation model (which may be the firstgeometrical compensation model) to model data. In other words, theobject model data used to generate the object(s) from which measurementshave been acquired may have itself been modified by a compensationmodel, and may be used to derive a new or modified compensation model.In other examples, the objects may be generated based on object modeldata which has not been modified using compensation parameters.

The data may be gathered from objects generated in one or more buildoperations carried out using the first additive manufacturing apparatus.In some examples, a single build operation may provide sufficient datato allow adaptation of a compensation model. The calibration objects maycomprise multiple instances of objects of a common design, and/or maycomprise objects of more than one design.

In some examples, the processor(s) providing the method may for examplehave an awareness of an expected data set. For example, the objects tobe printed by the first additive manufacturing apparatus may bepredetermined, for example comprising a predetermined calibration set ofobjects. In some examples, deviations may be scaled by anticipatedmeasurements. In some examples, the method may comprise validating thisdata. For example, data which indicates a large deviation may be due toa malfunction, noise introduced in the measurement process or the like.In such cases, it may be preferred to reject that data point (or indeedin some examples, to request a new data set entirely) to avoid derivinga new model based on data which is unlikely to be characteristic of thatadditive manufacturing apparatus. In some examples, an acceptable datarange (or the threshold for determining what constitutes a ‘large’deviation) may be derived in the context of the data set. For example, asingle large deviation in a data set which is otherwise indicative ofrelatively small deviations may be rejected, but a similarly largedeviation in a data set in which large deviations are relatively commonmay be maintained. This may allow a trend to be distinguished from, forexample, a measurement anomaly. In other examples abnormal measurements(which may lead to abnormal (for example large) estimations ofscale/offset) may be evaluated in relation to individual measurements.In some examples, if the data is incomplete (for example, at least athreshold number of expected measurements is missing or rejected), thedata set may be rejected.

Block 312 comprises deriving the geometrical transformations to beassociated with the second subset of locations based on the receivedindications of deviations. This may comprise, for example, determiningthe scaling and offset which would have resulted (on average) in thegenerated objects having their intended dimension, for example usingregression based data fitting techniques. If the object models for thecalibration objects were modified using a compensation model prior toobject generation, then this may be taken into account when deriving thegeometrical transformations.

Block 314 comprises replacing the data for at least a portion of thesecond subset of locations in the first geometrical compensation modelwith the transformations derived in block 312. This may result in amodified compensation model, which comprises a mix of ‘generic’geometrical transformation values and ‘specific’ geometricaltransformation values (i.e. specific to the first additive manufacturingapparatus). As noted above, if the object models for the calibrationobjects were modified using a compensation model prior to objectgeneration, then this may be taken into account when deriving thegeometrical transformations. However, in some examples, the generic datamay, instead of being replaced, be combined with the transformationsderived in block 312 to generate modified data for the second subset oflocations.

In some examples, rather than replacing geometrical transformations, theindication of deviations derived in block 310 may be used as part of adata set which includes ‘generic’ data—i.e. indications of deviationsderived from a plurality of additive manufacturing apparatus—for thefirst subset of locations. In such examples, the indication ofdeviations derived in block 310 may be combined with that data set whichmay be used to derive the first geometrical compensation model, ratherthan being used to adapt an existing geometrical compensation model.

This method may therefore generate apparatus-specific ‘corrections’ tothe first geometrical compensation model. The ‘corrections’ may bederived to compensate for the deviations from the expected measurementswhich are different to those compensated for by the first geometricalcompensation model. Such ‘corrections’ may be embodied as offsetvalue(s) and/or scaling factor(s), which in some examples may be addedor combined with offset value(s) and/or scaling factor(s) set out in thefirst geometrical compensation model. In other examples, a method may bemay generate a geometrical compensation model from a mix of generic datafor some locations and specific data for other locations

In some examples, the apparatus-specific geometrical compensation modelmay be derived remotely from the additive manufacturing apparatus forwhich the model has been tailored, and transmitted thereto, for exampleover a network or the like. A particular general geometricalcompensation model or data set maybe modified differently for each of aplurality of specific additive manufacturing apparatus.

As mentioned above, the first geometrical compensation model may bederived from a training set made up of measurements taken from objectsgenerated by a plurality of different additive manufacturing apparatus.In some examples, these additive manufacturing apparatus may be of thesame class of additive manufacturing apparatus as one another, and thefirst additive manufacturing apparatus may comprise a specific exampleof the class of additive manufacturing apparatus.

By using an apparatus-specific geometrical compensation model, a greaterdegree of geometrical accuracy may be obtained. Moreover, by modifyingexisting geometrical compensation model data (for example, a model or adata set which maybe basis for deriving such a model), an improvementmay be made based on a relatively small number of test/calibrationobjects. For example, the inventors have shown that a generic model maybe improved in this way using measurements obtained from around 1-20objects, with a number of measurements being acquired in each direction.In practice at least three measurements may be acquired for each objectin each direction to infer a scale and an offset value for that objectin that direction. If different adjustments are to be made to parametersrelating to different object generation conditions, a larger number ofobjects may be generated to ensure any adjustments made to the existinggeometrical compensation data are robust.

In an example, a ‘full’ calibration batch may comprise around 100-150objects, each being an instance of one of around five different designs.The number of measurements of each object depends on the design, but maybe in the range 10-30, giving around 2000 measurements for eachcalibration batch. However, if instead 40-60 objects were generated (forexample in or near the 40% of locations for calibration objectsassociated with the highest variability) and around 800 measurementsobtained, this could be used to adapt a compensation model and provide anoticeable improvement with respect to the dimensional accuracy in asubsequent build operation for the particular apparatus. Indeed, usingthe methods set out herein, the number of generated objects may berelatively low and an improvement may still be apparent.

In some examples, while the methods herein may reduce the number ofcalibration objects generated, there may be no reduction on the numberof measurements for an object in a given position.

Block 316 comprises determining a second spatial arrangement of objectsto be generated by the first additive manufacturing apparatus and block318 comprises determining, for each object, a geometrical transformationto be applied thereto based on the geometrical compensation modelgenerated in block 314 and the intended location of generation of thatobject. In this case, all of the objects may be ‘production’ objectsrather than including calibration objects.

In this example, when an object is to be generated at a location betweenthe locations of the set of locations, the method comprisesinterpolating a geometrical transformation based on the geometricaltransformation values associated with at least two locations of the setof locations. In some examples, geometrical transformation values of thefour closest locations may be interpolated to give an average, weightedby distance from each locating.

As noted above, it may be that not all the locations of the secondsubset of locations have new data associated therewith. In someexamples, the generic data may be deleted, and objects generated at ornear such locations may have transformations applied based oninterpolation. One example in this regard is discussed in greater detailin relation to FIG. 4 below.

Block 320 comprises generating the objects of the second spatialarrangement, for example as described in relation to block 308 above.

By using a suitable compensation model—i.e. a compensation model whichis tailored to a particular apparatus—to modify object model data, anobject once formed may end up being closer to an intended size.

In some examples, the methods set out herein may be combined with othermethods of object model modification. For example, a modificationfunction may be employed in the vicinity, or locality, of smallfeatures. An erosion of such small features may result in anunacceptable reduction in their size, either obliterating the feature orrendering it too small to fuse or too delicate to survive cleaningoperations. For example, if a feature has a dimension of around 0.5 mm,this may correspond to 12 voxels at 600 dpi. If three or four voxels areeroded from the side of such a small feature, it will lose approximately50 to 60% of its cross-section, reducing its size to less than 0.3 mm.Such a feature may be too small to survive cleaning operations. Thus, insome examples, other functions may be used to ensure that small featuresare preserved.

FIG. 4 is an example of a method in which a zone comprising a pluralityof neighbouring locations are determined to have the second, higher,level of variability.

In this case, block 402 comprises determining, by at least oneprocessor, a third subset of locations, wherein each location of thethird subset of locations is a location of the second subset oflocations and is adjacent to a location of the second subset oflocations. In other words, the third subset of locations is selectedfrom the second subset, with the qualifying feature being that at leastone neighbour of each location is also in the second subset. Expressedanother way, the locations of the third subset of locations representclusters of two or more locations associated with relatively highvariability in deviations from anticipated dimensions.

Block 404 comprises determining, by at least one processor, ageometrical transformation to be associated with at least one locationof the third subset of locations based on interpolation of geometricaltransformations for at least one location of the first subset oflocations and data derived from an object generated by the firstadditive manufacturing apparatus. The interpolation may not be a linearinterpolation and may be biased such that the effect of a value of ageometrical transformation which has been determined using objectsgenerated by the first additive manufacturing apparatus degrades on acurve, for example according to an inverse square rule or the like,rather than in a linear fashion, for example using non-linearinterpolation.

In particular, it may be that, for any one cluster of locations of thesecond subset, the number of locations in which a calibration object isgenerated may be less than the number in the cluster. As noted above,even in the case of a full calibration batch, the number of calibrationobjects may be less than the number of locations having predeterminedtransformations associated therewith, and the data for locations ornodes may be interpolated from measured data.

In some examples herein, a geometrical transformation to be associatedwith at least one location of the third subset of locations may becharacterised based on at least one measured object generated by thefirst additive manufacturing apparatus from within the zone, and atleast one ‘generic’ data point. In other words, the geometricaltransformations for at least one location (and in some examples, alllocations if the location of a calibration object does not match alocation of a node) of a cluster may then be inferred, for exampleinterpolated from surrounding locations, which may include thegeometrical transformation characterised for a single location, or a fewlocations, in each cluster.

For example, the ‘generic’ data for a zones of high variability may bedisregarded, and replaced with new data, which may comprise interpolateddata based on at least one object generated using the first additivemanufacturing apparatus. In some examples, at least one ‘generic’ datavalue may be replaced with a specific data value based on an objectgenerated at a corresponding location. This may allow zones of highvariability to be characterised using fewer calibration objects(reducing use of time and resources), while nevertheless tailoring thecharacterisation of the zone to the first additive manufacturingapparatus.

FIG. 5 shows an apparatus 500 comprising processing circuitry 502, theprocessing circuitry 502 comprising a geometrical compensation modelgeneration module 504.

In use of the apparatus 500, the geometrical compensation modelgeneration module 504 merges first geometrical compensation data andsecond geometrical compensation data to determine a geometricalcompensation model for use in compensating for anticipated deformationsin object generation by a first additive manufacturing apparatus. Thederived geometrical transformation model may have any of the features ofthe geometrical compensation models discussed above, and may be intendedto compensate for anticipated departures in dimensions from intendeddimensions on object generation.

The first geometrical compensation data comprises geometricalcompensation data associated with a first predetermined set of locationswithin a fabrication chamber and is derived based on indications ofdeviations between intended object dimensions and generated objectdimensions of objects generated by a plurality of additive manufacturingapparatus. For example, this may comprise ‘generic’compensation/deviation data as described above.

The second geometrical compensation data comprises geometricalcompensation data associated with a second predetermined set oflocations within a fabrication chamber and is derived based onindications of deviations between intended object dimensions andgenerated object dimensions of objects generated by the first additivemanufacturing apparatus. For example, this may comprise ‘specific’compensation/deviation data as described above.

The first geometrical compensation data comprises data associated withindications of deviations which have a variability between additivemanufacturing apparatus which is below a threshold. The threshold may beapplied to derived transformation parameters such as offset and/orscaling factors. In some examples, the second geometrical compensationdata comprises data associated with indications of deviations which havea variability between additive manufacturing apparatus which is abovethe threshold, and/or at a higher level of variability that the firstgeometrical compensation data.

FIG. 6 shows an apparatus 600 comprising processing circuitry 602, theprocessing circuitry 602 comprising the geometrical compensation modelgeneration module 504 as described in relation to FIG. 5 as well as adata acquisition module 604 and an object generation instructions module606.

In use of the apparatus 600, the data acquisition module 604 acquiresdata indicative of deviations between intended object dimensions andgenerated object dimensions for objects generated by the first additivemanufacturing apparatus to provide the second geometrical compensationdata. For example, this may comprise a 3D scanner, or a data inputterminal for receiving input measurements, and/or may determine thedeviations from such measurements.

In use of the apparatus 600, the object generation instructions module606 determines object generation instructions for generating at leastone object, the object generation instructions specifying an amount ofprint agent to be applied to each of a plurality of locations on a layerof build material, and being determined based on object model datamodified using the geometrical transformation model. For example, thismay comprise determining a geometrical transformation for an object tobe generated using additive manufacturing based on the geometricaltransformation model and modifying the object model data using thegeometrical transformation

The object generation instructions (or print instructions), which may bederived by the object generation instructions module 606 may control theadditive manufacturing apparatus 600 to generate each of the pluralityof layers of the object. This may for example comprise specifying areacoverage(s) for print agents such as fusing agents, colorants, detailingagents and the like. In some examples, such object generation parametersare associated with object model sub-volumes. In some examples, otherparameters, such as any, or any combination of heating temperatures,build material choices, an intent of the print mode, and the like, maybe specified. In some examples, halftoning may be applied to determinewhere to place fusing agent or the like. The object generationinstructions may be specified in association with sub-volumes (e.g.voxels as described above). In some examples, the object generationinstructions comprise a print agent amount associated with sub-volumes.

In some examples, the apparatus 600 may comprise additive manufacturingapparatus, which may generate an object based on the object generationinstructions. For example, the apparatus 600, in use thereof, maygenerate the object in a plurality of layers (which may correspond torespective slices of an object model) according to object generationinstructions. The apparatus 600 may for example generate an object in alayer-wise manner by selectively solidifying portions of layers of buildmaterial. The selective solidification may in some examples be achievedby selectively applying print agents, for example through use of‘inkjet’ liquid distribution technologies, and applying energy, forexample heat, to the layer. In other examples, heat may be selectivelyapplied, and/or chemical agents such as curing or binding agents may beapplied. The apparatus 600 may comprise additional components not shownherein, for example any or any combination of a fabrication chamber, aprint bed, printhead(s) for distributing print agents, a build materialdistribution system for providing layers of build material, energysources such as heat lamps and the like.

In some examples, object generation may comprise a fusing process, forexample a thermal fusing process in which heat is applied.

The apparatus 500, 600 described in FIG. 5 or 6 may comprise a memoryresource to store at least one geometrical compensation model. Forexample, this may comprise any or any combination of mapping resources,transformation vector(s), compensation parameter(s), algorithm(s) or thelike, as have been described above. The geometrical compensation modelsmay be intended to compensate for object deformation in additivemanufacturing, wherein each geometrical compensation model may specifyor determine a compensation transformation to apply based onpredetermined criteria, which may differ between models. The geometricalcompensation model(s) may have any of the features of the geometricalcompensation models discussed above.

The processing circuitry 502, 602 or the modules thereof may carry outany of the blocks of FIG. 1, 3 or 4 .

FIG. 7 shows a tangible machine-readable medium 700 associated with aprocessor 702. The machine-readable medium 700 comprises instructions704 which, when executed by the processor 702, cause the processor 702to carry out tasks. In this example, the instructions 704 compriseinstructions 706 to cause the processor 702 to modify data forcharacterising geometrical modifications to compensate for anticipateddimensional deviations in object generation, wherein the geometricalmodification is to vary based on location of generation and the data isderived based on measurements of dimensional deviation of objectsgenerated by a plurality of additive manufacturing apparatus for usewith a particular additive manufacturing apparatus. The modificationcomprises replacing at least one data value associated with a locationof dimensional deviation variation above a threshold with a data valuederived from measurements of dimensional deviation of objects generatedby the particular apparatus. The instructions 706 may be instructions togenerate a geometrical compensation model which comprises at least onescaling factor and/or at least one offset value associated with each ofa plurality of predetermined locations. In some examples, each of thescaling factors and each of the offset values may be associated with oneof three orthogonal axes. In some examples, the modification is based ondimensions of a plurality of test (or calibration) objects, which maybe, or may be taken from, a predetermined set of calibration objects.

In some examples, the instructions 704 comprise instructions which, whenexecuted, cause the processor 702 to cause an additive manufacturingapparatus to generate at least one calibration object at or near alocation of dimensional deviation variation above a threshold.

In some examples, the instructions when executed cause the processor 702to carry out any of the blocks of FIG. 1, 3 or 4 . In some examples, theinstructions may cause the processor 702 to act as any part of theprocessing circuitry 502, 602 of FIG. 5 or FIG. 6 .

Examples in the present disclosure can be provided as methods, systemsor machine-readable instructions, such as any combination of software,hardware, firmware or the like. Such machine-readable instructions maybe included on a computer readable storage medium (including but notlimited to disc storage, CD-ROM, optical storage, etc.) having computerreadable program codes therein or thereon.

The present disclosure is described with reference to flow charts and/orblock diagrams of the method, devices and systems according to examplesof the present disclosure. Although the flow diagrams described aboveshow a specific order of execution, the order of execution may differfrom that which is depicted. Blocks described in relation to one flowchart may be combined with those of another flow chart. It shall beunderstood that each block in the flow charts and/or block diagrams, aswell as combinations of the blocks in the flow charts and/or blockdiagrams can be realized by machine-readable instructions.

The machine-readable instructions may, for example, be executed by ageneral purpose computer, a special purpose computer, an embeddedprocessor or processors of other programmable data processing devices torealize the functions described in the description and diagrams. Inparticular, a processor or processing apparatus may execute themachine-readable instructions. Thus functional modules of the apparatus(such as the geometrical compensation model generation module 504, thedata acquisition module 604 and the object generation instructionsmodule 606) may be implemented by a processor executing machine-readableinstructions stored in a memory, or a processor operating in accordancewith instructions embedded in logic circuitry. The term ‘processor’ isto be interpreted broadly to include a CPU, processing unit, ASIC, logicunit, or programmable gate array etc. The methods and functional modulesmay all be performed by a single processor or divided amongst severalprocessors.

Such machine-readable instructions may also be stored in a computerreadable storage that can guide the computer or other programmable dataprocessing devices to operate in a specific mode.

Machine-readable instructions may also be loaded onto a computer orother programmable data processing devices, so that the computer orother programmable data processing devices perform a series ofoperations to produce computer-implemented processing, thus theinstructions executed on the computer or other programmable devicesrealize functions specified by flow(s) in the flow charts and/orblock(s) in the block diagrams.

Further, the teachings herein may be implemented in the form of acomputer software product, the computer software product being stored ina storage medium and comprising a plurality of instructions for making acomputer device implement the methods recited in the examples of thepresent disclosure.

While the method, apparatus and related aspects have been described withreference to certain examples, various modifications, changes,omissions, and substitutions can be made without departing from thespirit of the present disclosure. It is intended, therefore, that themethod, apparatus and related aspects be limited by the scope of thefollowing claims and their equivalents. It should be noted that theabove-mentioned examples illustrate rather than limit what is describedherein, and that those skilled in the art will be able to design manyalternative implementations without departing from the scope of theappended claims. Features described in relation to one example may becombined with features of another example.

The word “comprising” does not exclude the presence of elements otherthan those listed in a claim, “a” or “an” does not exclude a plurality,and a single processor or other unit may fulfil the functions of severalunits recited in the claims.

The features of any dependent claim may be combined with the features ofany of the independent claims or other dependent claims.

1. A method comprising: acquiring, by a processor and for a set oflocations in a fabrication chamber for additive manufacturing, dataidentifying a first subset of locations associated with a first level ofvariability in deformations in object generation and a second subset oflocations associated with a second level of variability in deformationsobject generation; wherein the second level is greater than the firstlevel; deriving, by a processor, a geometrical compensation model tocompensate for anticipated deformations in object generation by a firstadditive manufacturing apparatus; the geometrical compensation modelcomprising geometrical transformations to apply to object model datarepresenting at least a portion of an object to be generated, whereineach geometrical transformation is associated with a location of the setof locations; wherein the first subset of locations are associated withgeometrical transformations determined based on indications ofdeviations between intended object dimensions and generated objectdimensions of objects generated by a plurality of additive manufacturingapparatus; and the second subset of locations is associated withgeometrical transformations determined based on indications ofdeviations between intended object dimensions and generated objectdimensions of objects generated by the first additive manufacturingapparatus.
 2. A method according to claim 1 wherein determining thegeometrical transformation for the second subset of locations comprisesdetermining object generation instructions for generating objects in ornear at least one of the second subset of locations.
 3. A methodaccording to claim 1 comprising acquiring, at at least one processor, anindication of deviations between intended object dimensions andgenerated object dimensions for objects generated by the first additivemanufacturing apparatus in or near at least one of the second subset oflocations; and deriving the geometrical transformations to be associatedwith the second subset of locations based on the received indications ofdeviations.
 4. A method according to claim 1 wherein the geometricaltransformations for the first subset of locations are taken from a firstgeometrical compensation model including geometrical transformations forthe set of locations; wherein each location is associated with avariability parameter derived from the measurements of deformation ofobjects generated by the plurality of additive manufacturing apparatus.5. A method according to claim 4 comprising determining the variabilityparameter for each location, wherein the variability parameter comprisesa measure of standard deviation.
 6. A method according to claim 1comprising: determining a spatial arrangement of objects to be generatedby the first additive manufacturing apparatus in a common buildoperation; and determining, for each object, a geometricaltransformation to be applied thereto based on the geometricalcompensation model and the intended location of generation of thatobject.
 7. A method according to claim 6 comprising, when an object isto be generated at a location between the locations of the set oflocations, interpolating a geometrical transformation based on thegeometrical transformation values associated with at least two locationsof the set of locations.
 8. A method according to claim 1 comprisingsetting a threshold defining the first and second levels of variability.9. A method according to claim 1 wherein the set of locations comprisesa third subset of locations, wherein each location of the third subsetof locations is a location of the second set of locations, the methodcomprising determining at least one geometrical transformation to beassociated with a location of the third subset of locations based oninterpolation of geometrical transformations for at least one locationof the first subset of locations and data derived from an objectgenerated by the first additive manufacturing apparatus.
 10. Apparatuscomprising processing circuitry, the processing circuitry comprising: ageometrical compensation model generation module, wherein thecompensation model generation module is to merge first geometricalcompensation data and second geometrical compensation data to determinea geometrical compensation model for use in compensating for anticipateddeformations in object generation by a first additive manufacturingapparatus, wherein the first geometrical compensation data comprisesgeometrical compensation data associated with a first predetermined setof locations within a fabrication chamber and is derived based onindications of deviations between intended object dimensions andgenerated object dimensions of objects generated by a plurality ofadditive manufacturing apparatus; and the second geometricalcompensation data comprises geometrical compensation data associatedwith a second predetermined set of locations within a fabricationchamber and is derived based on indications of deviations betweenintended object dimensions and generated object dimensions of objectsgenerated by the first additive manufacturing apparatus; wherein thefirst geometrical compensation data comprises data associated withindications of deviations which have a variability between additivemanufacturing apparatus which is below a threshold.
 11. Apparatusaccording to claim 10 further comprising a data acquisition module toacquire data indicative of deviations between intended object dimensionsand generated object dimensions for objects generated by the firstadditive manufacturing apparatus to provide the second geometricalcompensation data.
 12. Apparatus according to claim 10 furthercomprising an object generation instructions module to determine objectgeneration instructions for generating at least one object, the objectgeneration instructions specifying an amount of print agent to beapplied to each of a plurality of locations on a layer of buildmaterial, and being determined based on object model data modified usingthe geometrical transformation model.
 13. A machine-readable mediumstoring instructions which, when executed by a processor, cause theprocessor to: modify data for characterising geometrical modificationsto compensate for anticipated dimensional deviations in objectgeneration, wherein the geometrical modification is to vary based onlocation of generation and the data is derived based on measurements ofdimensional deviation of objects generated by a plurality of additivemanufacturing apparatus for use with a particular additive manufacturingapparatus; wherein the modification comprises replacing at least onedata value associated with a location of dimensional deviation variationabove a threshold with a data value derived from measurements ofdimensional deviation of objects generated by the particular additivemanufacturing apparatus.
 14. A machine-readable medium according toclaim 13 wherein the instructions are to modify the data to generate ageometrical compensation model which comprises at least one scalingfactor and/or at least one offset value associated with each of aplurality of predetermined locations.
 15. A machine-readable mediumaccording to claim 13 wherein instructions further comprise instructionsto cause an additive manufacturing apparatus to generate at least onecalibration objects in or near a location of dimensional deviationvariation above a threshold.